Model Serving
In the previous chapters, we trained two models based on the USA Housing dataset.
Now we will reload the linear regression model to do some predictions.
Create connection to ML Aide webserver
Our code will be written in a new file named serving.py. In the beginning,
we will create a connection to the ML Aide webserver.
from mlaide import MLAideClient, ConnectionOptions
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
import numpy as np
options = ConnectionOptions(
server_url='http://localhost:8881/api/v1', # the ML Aide demo server runs on port 8881 per default
api_key='<your api key>'
)
mlaide_client = MLAideClient(project_key='usa-housing', options=options)
Load Model and Pipeline
To do some predictions we want to use the linear regression model. But before we predict values with the model, we have to use the sklearn pipeline to transform our input vectors. The pipeline was also stored in ML Aide. Thus, we can load both from ML Aide.
# read the model
lin_reg: LinearRegression = mlaide_client.load_model('linear regression')
# read the pipeline containing the standard scaler
pipeline: Pipeline = mlaide_client.load_model('pipeline')
Predict Values
Now we are ready to use our model. In this case, we will hardcode a house area for our prediction. In real-world scenarios, we would get the input from HTTP requests or something similar.
# create some data for prediction
data = np.array([[80000, 6.32, 7.4, 4.24, 25000]])
# The values are
# - Avg. Area Income
# - Avg. Area House Age
# - Avg. Area Number of Rooms
# - Avg. Area Number of Bedrooms
# - Area Population
# predict the house price
data = pipeline.transform(data)
pred = lin_reg.predict(data)
print(pred) # output is: [1415072.9471789]
Summary
In this chapter we
- loaded the linear regression model
- loaded the sklearn pipeline
- predicted a value using the model
Your code should look like the following snippet shows.
Code
from mlaide import MLAideClient, ConnectionOptions
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
import numpy as np
options = ConnectionOptions(
server_url='http://localhost:8881/api/v1', # the ML Aide demo server runs on port 8881 per default
api_key='<your api key>'
)
mlaide_client = MLAideClient(project_key='usa-housing', options=options)
# read the model
lin_reg: LinearRegression = mlaide_client.load_model('linear regression')
# read the pipeline containing the standard scaler
pipeline: Pipeline = mlaide_client.load_model('pipeline')
# create some data for prediction
data = np.array([[80000, 6.32, 7.4, 4.24, 25000]])
# The values are
# - Avg. Area Income
# - Avg. Area House Age
# - Avg. Area Number of Rooms
# - Avg. Area Number of Bedrooms
# - Area Population
# predict the house price
data = pipeline.transform(data)
pred = lin_reg.predict(data)
print(pred)
from mlaide import MLAideClient, ConnectionOptions, ArtifactRef
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression, Lasso
from sklearn import metrics
# create connection
options = ConnectionOptions(
server_url='http://localhost:8881/api/v1', # the ML Aide demo server runs on port 8881 per default
api_key='<your api key>'
)
mlaide_client = MLAideClient(project_key='usa-housing', options=options)
# get housing dataset
dataset_bytes = mlaide_client.get_artifact('USA housing dataset', version=None).load('data/housing.csv')
housing_data = pd.read_csv(dataset_bytes)
artifact_ref = ArtifactRef(name="USA housing dataset", version=1)
run_pipeline_setup = mlaide_client.start_new_run(experiment_key='linear-regression',
run_name='pipeline setup',
used_artifacts=[artifact_ref])
# train test split
X = housing_data[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',
'Avg. Area Number of Bedrooms', 'Area Population']]
y = housing_data['Price']
test_size=0.3
random_state=42
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
run_pipeline_setup.log_parameter('test_size', test_size)
run_pipeline_setup.log_parameter('random_state', random_state)
pipeline = Pipeline([
('std_scalar', StandardScaler())
])
X_train = pipeline.fit_transform(X_train)
X_test = pipeline.transform(X_test)
run_pipeline_setup.log_model(pipeline, model_name="pipeline")
run_pipeline_setup.set_completed_status()
# linear regression
dataset_artifact_ref = ArtifactRef(name="USA housing dataset", version=1)
pipeline_artifact_ref = ArtifactRef(name="pipeline", version=1)
run_linear_regression = mlaide_client.start_new_run(experiment_key='linear-regression',
run_name='linear regression',
used_artifacts=[dataset_artifact_ref, pipeline_artifact_ref])
lin_reg = LinearRegression(normalize=True)
lin_reg.fit(X_train,y_train)
run_linear_regression.log_model(lin_reg, 'linear regression')
test_pred = lin_reg.predict(X_test)
train_pred = lin_reg.predict(X_train)
mae = metrics.mean_absolute_error(y_test, test_pred)
mse = metrics.mean_squared_error(y_test, test_pred)
rmse = np.sqrt(metrics.mean_squared_error(y_test, test_pred))
r2 = metrics.r2_score(y_test, test_pred)
cross_validation = cross_val_score(LinearRegression(), X, y, cv=10).mean()
run_linear_regression.log_metric('mae', mae)
run_linear_regression.log_metric('mse', mse)
run_linear_regression.log_metric('rmse', rmse)
run_linear_regression.log_metric('r2', r2)
run_linear_regression.log_metric('cross validation', cross_validation)
run_linear_regression.set_completed_status()
# lasso regression
dataset_artifact_ref = ArtifactRef(name="USA housing dataset", version=1)
pipeline_artifact_ref = ArtifactRef(name="pipeline", version=1)
run_lasso = mlaide_client.start_new_run(experiment_key='lasso-regression',
run_name='lasso regression',
used_artifacts=[dataset_artifact_ref, pipeline_artifact_ref])
alpha = 0.1
precompute = True
positive = True
selection = 'random'
random_state = 42
run_lasso.log_parameter('alpha', alpha)
run_lasso.log_parameter('precompute', precompute)
run_lasso.log_parameter('positive', positive)
run_lasso.log_parameter('selection', selection)
run_lasso.log_parameter('random state', random_state)
model = Lasso(alpha=alpha,
precompute=precompute,
positive=positive,
selection=selection,
random_state=random_state)
model.fit(X_train, y_train)
run_lasso.log_model(model, 'lasso')
test_pred = model.predict(X_test)
train_pred = model.predict(X_train)
mae = metrics.mean_absolute_error(y_test, test_pred)
mse = metrics.mean_squared_error(y_test, test_pred)
rmse = np.sqrt(metrics.mean_squared_error(y_test, test_pred))
r2 = metrics.r2_score(y_test, test_pred)
cross_validation = cross_val_score(Lasso(), X, y, cv=10).mean()
run_lasso.log_metric('mae', mae)
run_lasso.log_metric('mse', mse)
run_lasso.log_metric('rmse', rmse)
run_lasso.log_metric('r2', r2)
run_lasso.log_metric('cross validation', cross_validation)
run_lasso.set_completed_status()
from mlaide import MLAideClient, ConnectionOptions
import pandas as pd
options = ConnectionOptions(
server_url='http://localhost:8881/api/v1', # the ML Aide demo server runs on port 8881 per default
api_key='<your api key>'
)
mlaide_client = MLAideClient(project_key='usa-housing', options=options)
run_data_preparation = mlaide_client.start_new_run(experiment_key='linear-regression', run_name='data preparation')
housing_data = pd.read_csv('data/housing.csv')
# add dataset as artifact
artifact = run_data_preparation.create_artifact(name="USA housing dataset", artifact_type="dataset", metadata={})
run_data_preparation.add_artifact_file(artifact, 'data/housing.csv')
run_data_preparation.set_completed_status()
a lot of housing data